Mapping the distribution of subprime borrowers by county
The “subprime credit population”—those with credit scores below 650—received much attention during and after the Great Recession of 2007-2009. Are these borrowers concentrated in certain areas or evenly distributed across the country? FRED’s county-level data from Equifax maps out the percentage of each county’s population that’s classified as subprime. The geographic disparities are quite large. At the high end, in Kenedy County, Texas, almost 56% of the population has a subprime credit score. At the low end, in Hooker County, Nebraska, only 3% of the population has a subprime credit score. Overall, the subprime population is more common in Southern states, but there are exceptions. Big Horn County, Montana, is 35% subprime, resembling Hardin County, Texas, and Marshall County, Tennessee. But some of Big Horn’s neighboring counties in Montana—for example, Carbon County (16%) and Yellowstone County (23%)—have much smaller subprime populations.
How this map was created: From GeoFRED, select county-level data and choose “Equifax Subprime Credit Population” in the drop-down menu.
Suggested by Guillaume Vandenbroucke.
An update on the Beveridge curve
Three and a half years ago, we published a blog post about the Beveridge curve featuring the graph above, which shows how job vacancies and unemployment relate to each other. Each dot represents their values at a particular date. Beveridge’s theory is that these two measures don’t form a kinked line along the axes in a scatter plot, but rather a banana shape. This shape occurs because of delays and frictions in the job market: Vacancies and job seekers take time to intersect, as there may be mismatches in terms of job location and qualifications, for example. The graph above doesn’t show the expected full banana because the available sample period just wasn’t long enough. So, we revisit this idea by updating the graph, shown below. The banana, although not very smooth, is now complete.
How this graph was created: Search for “job openings” and add the series to the graph. From the “Edit Graph” section, add the second series by searching for and adding “civilian unemployment.” From the “Format” tab, choose “Scatter” for graph type. To connect the dots, choose a non-zero line width in the settings of the first series, which is where you can also adjust the size of the dots.
Suggested by Charley Kyd and Christian Zimmermann.
View on FRED, series used in this post:
Age differences across the U.S.
Much is said about the ethnic patchwork of the United States, but this map highlights another kind of diversity: age. FRED’s county-level data on the age distribution of the U.S. population show large variations across the country and also within states. The spread of the range is also surprising. The youngest median age, 21.4 years, is in Lexington City County, Virginia—a small county that hosts two universities. The oldest median age, 65.3 years, is in Sumter County, Florida, where over half the residents live in a giant retirement community. Florida is expected to have a high median age, but other states also have counties with high median ages, including the admittedly small counties of Mineral, Colorado (60.9 years); Highland, Virginia (59.0 years); and Hooker, Nebraska (57.9 years).
How this map was created: Go to GeoFRED, select county-level data, and find median age population data in the drop-down menu.
Suggested by Christian Zimmermann.